Alcohol and tobacco use in relation to gastric cancer: a prospective study of men in Shanghai, China.
Epidemiologic findings of tobacco and alcohol use in relation to gastric cancer are inconsistent. Well-designed prospective studies examining their relationship are sparse.
The association between cigarette smoking/alcohol intake and gastric cancer risk was examined in a population-based prospective cohort of 18,244 middle-aged and older men in Shanghai, China, who were enrolled in the study during 1986-1989. After up to 20 years of follow-up, 391 incident gastric cancer cases were identified. Cox proportional hazards regression models were used to estimate hazard ratios (HR) and corresponding 95% confidence intervals (95% CI).
Ever smokers experienced a statistically significant increased risk of gastric cancer (HR, 1.59; 95% CI, 1.27-1.99) compared with nonsmokers after adjustment for alcohol intake and other confounders. Among nondrinkers, smokers experienced 80% increased risk of gastric cancer (HR, 1.81; 95% CI,1.36, 2.41). Conversely, heavy drinkers experienced a statistically significant increase in risk of gastric cancer (HR, 1.46; 95% CI, 1.05-2.04) among all subjects and a statistically nonsignificant 80% increased risk among never smokers. Further adjustment for Helicobacter pylori serology, serum levels of beta-carotene and vitamin C, and urinary level of total isothiocyanates in combination with glutathione S-transferase (GST) M1 and GSTT1 genotypes did not materially change the associations between smoking/alcohol consumption and gastric cancer risk.
These results suggest that cigarette smoking and alcohol consumption may exert independent effects on the development of gastric cancer in this high-risk population.
Modification of these lifestyle choices may reduce the incidence of gastric cancer.
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Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Masonic Cancer Center, Minneapolis, Minnesota 55455, USA. firstname.lastname@example.org
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Body Mass Index
Proportional Hazards Models
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